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Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework
Author(s) -
Chatzistergos Sevastianos E.,
Andreadis Ioannis,
Nikita Konstantina S.
Publication year - 2018
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12281
Subject(s) - computer science , pattern recognition (psychology) , mammography , artificial intelligence , support vector machine , classifier (uml) , identification (biology) , local binary patterns , binary number , principal component analysis , feature vector , data mining , image (mathematics) , mathematics , histogram , medicine , botany , arithmetic , cancer , breast cancer , biology
A method based on the combination of Local Binary Pattern operator and radial lengths is presented aiming at the identification of Architectural Distortions ( A D s ) in mammograms. Local Binary Pattern operator, a number of its variants, and radial lengths are combined together producing a high‐dimensional feature space. A process, based on the combination of Principal Component Analysis and t test, is used to effectively transform feature space and reveal the most descriptive features. The classification step is performed using a Support Vector Machine classifier. Open access databases (Mammographic Image Analysis Society and Digital Database for Screening Mammography) are used through an exhaustive evaluation framework that aims at eliminating both mammogram selection bias and limited subtlety variation, thus enabling a fair and complete comparison procedure. Furthermore, in order to provide a test bed for future comparisons, a dataset is constructed from all the available A D Regions Of Interest in Digital Database for Screening Mammography (163 A D vs 375 Regions Of Interest from specific normal cases) and is used to further evaluate the performance of the proposed method. The method performed flawlessly and classified correctly all cases.